1 TADPOLE and BSWiMS

1.0.1 Loading the libraries

library("FRESA.CAD")
library(survival)
library(readxl)
library(igraph)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 Loading BSWiMS Results


load("./TADPOLE_BSWIMS_Results.RData")

pander::pander(table(TADPOLE_Conv_TRAIN$status))
0 1
184 92 
pander::pander(table(TADPOLE_Conv_TEST$status))
0 1
123 62 
par(op)

1.1.1 Learning BIC

bConvmBess <- BESS(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

pander::pander(bConvmBess$selectedfeatures)

ADAS13, MMSE, FAQ, APOE4, ABETA, ST2SV, ST9SV, M_ST34TA, M_ST38TA, M_ST62TA, M_ST23TS, M_ST25TS, M_ST34TS, M_ST15SA, M_ST24SA, M_ST26SA, M_ST31SA, M_ST30SV, M_ST53SV, RD_ST23TA, RD_ST129TA, RD_ST35TA, RD_ST43TA, RD_ST44TA, RD_ST45TA, RD_ST51TA, RD_ST13TS, RD_ST31TS, RD_ST129TS, RD_ST44TS, RD_ST47TS, RD_ST52TS, RD_ST54TS, RD_ST55TS, RD_ST57TS, RD_ST62TS, RD_ST25SA, RD_ST48SA, RD_ST52SA, RD_ST55SA, RD_ST57SA, RD_ST60SA, RD_ST23CV, RD_ST26CV, RD_ST47CV, RD_ST12SV, RD_ST21SV, RD_ST53SV and RD_ST66SV


ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.75 0.691 0.811 
pander::pander(prSurv$CILp)
median lower upper
0.795 0.72 0.859 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.127 -0.116 0.361 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.794 0.726 0.863 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 47 36 83
Test - 15 87 102
Total 62 123 185 

par(op)

1.1.2 Learning EBIC

bConvmBessE <- BESS_EBIC(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

pander::pander(bConvmBessE$selectedfeatures)

ADAS13, MMSE, FAQ, APOE4, ABETA, ST2SV, ST9SV, M_ST34TA, M_ST38TA, M_ST62TA, M_ST23TS, M_ST25TS, M_ST34TS, M_ST15SA, M_ST24SA, M_ST26SA, M_ST31SA, M_ST30SV, M_ST53SV, RD_ST23TA, RD_ST129TA, RD_ST35TA, RD_ST43TA, RD_ST44TA, RD_ST45TA, RD_ST51TA, RD_ST13TS, RD_ST31TS, RD_ST129TS, RD_ST44TS, RD_ST47TS, RD_ST52TS, RD_ST54TS, RD_ST55TS, RD_ST57TS, RD_ST62TS, RD_ST25SA, RD_ST48SA, RD_ST52SA, RD_ST55SA, RD_ST57SA, RD_ST60SA, RD_ST23CV, RD_ST26CV, RD_ST47CV, RD_ST12SV, RD_ST21SV, RD_ST53SV and RD_ST66SV


ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.826 0.772 0.873 
pander::pander(prSurv$CILp)
median lower upper
0.86 0.808 0.911 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.265 0.0144 0.483 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.86 0.804 0.915 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 50 39 89
Test - 12 84 96
Total 62 123 185 

par(op)

1.1.3 Learning GS

bConvmBessGS <- BESS_GSECTION(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

1-th iteration s.left:1 s.split:31 s.right:49-th iteration s.left:31 s.split:42 s.right:49-th iteration s.left:42 s.split:46 s.right:49-th iteration s.left:46 s.split:48 s.right:49


pander::pander(bConvmBessGS$selectedfeatures)

ADAS11, MMSE, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, WholeBrain, MidTemp, TAU, PTAU, ST2SV, ST5SV, M_ST13TA, M_ST14TA, M_ST24TA, M_ST34TS, M_ST58TS, M_ST15SA, M_ST39SA, M_ST56SA, M_ST13CV, M_ST24CV, M_ST40CV, M_ST58CV, M_ST29SV, M_ST66SV, RD_ST34TA, RD_ST35TA, RD_ST45TA, RD_ST51TA, RD_ST58TA, RD_ST13TS, RD_ST23TS, RD_ST25TS, RD_ST35TS, RD_ST44TS, RD_ST47TS, RD_ST52TS, RD_ST55TS, RD_ST62TS, RD_ST13SA, RD_ST24SA, RD_ST46SA, RD_ST51SA, RD_ST52SA, RD_ST26CV, RD_ST48CV and RD_ST65SV


ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.811 0.756 0.86 
pander::pander(prSurv$CILp)
median lower upper
0.845 0.786 0.898 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.279 0.046 0.487 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.847 0.79 0.904 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 51 38 89
Test - 11 85 96
Total 62 123 185 

par(op)

1.1.4 Saving the enviroment

save.image("./TADPOLE_LASSO_Results.RData")